Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles

Reinforcement learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time,...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 18; p. 3773
Main Authors Jang, Sun-Ho, Ahn, Woo-Jin, Kim, Yu-Jin, Hong, Hyung-Gil, Pae, Dong-Sung, Lim, Myo-Taeg
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Algorithms
Autonomous vehicles
Constraints
Data mining
Decision making
Deep learning
Driverless cars
Efficiency
Knowledge
Machine learning
Neural networks
Performance enhancement
Reinforcement learning (Machine learning)
System effectiveness
System failures
Teaching methods
Unmanned vehicles
South Korea
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Summary:Reinforcement learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like hindsight experience replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines obstacle-dependent Gaussian (ODG) RL, soft actor-critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. With these solutions in place, the ultimate aim of this work is to improve the performance of reinforcement learning and develop a swift, stable, and robust learning method for implementing autonomous driving systems that can effectively adapt to various environments and overcome the constraints of direct RL utilization in autonomous vehicles. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12183773